March 11th, 2026 / News
AI at scale, within the EHR, without adding complexity
That’s where artificial intelligence is headed today, says EHR vendor CliniComp’s Sandra Johnson. HIMSS26 is highlighting the need for AI that is integrated, interoperable, clinically designed and operationally accountable, she adds.
Much is being said at the 2026 HIMSS Global Health Conference & Exposition about a shift from experimental AI adoption to operationalized, governed AI embedded in core systems. This shift is putting AI governance and clean data to work at enterprise scale, underpinned by standards-based interoperability, said Sandra Johnson, senior vice president, client services, at CliniComp, an AI-powered EHR vendor, in booth 6021 at HIMSS26 this week in Las Vegas.
“Native AI is no longer a concept demonstration,” she stated. “CIOs are now focused on how to responsibly deploy transformative AI at scale, within the EHR, without adding complexity, cost or fragmentation. That means clear guardrails for AI, tighter security against new threats, and FHIR/HL7-driven data exchange you can trust.
“The industry is moving from ‘What can AI do?’ to ‘How do we integrate AI responsibly, natively and at the point of care?'” she continued. “Add-on AI fragments data, widens security gaps and breaks clinician trust when it sits outside daily workflows. HIMSS26 is highlighting the need for AI that is integrated, interoperable, clinically designed and operationally accountable.”
Beware add-on complexity
CIOs at hospitals and health systems should be prioritizing an EHR with native AI over add-on complexity and linking AI use cases to specific, measurable operational impact, Johnson said.
“Native AI, integrated into the EHR, has already been shown to maintain continuous uptime and leverage open APIs, so new capabilities plug in without extra interfaces,” she continued. “Adding multiple third-party AI tools increases technical debt and vendor sprawl.
“CIOs should first evaluate whether AI capabilities are embedded within the EHR architecture itself, ensuring consistent data access, unified security controls and streamlined workflows,” she added. “AI should directly reduce administrative workload, accelerate time to collections and improve clinical decision-making – not create parallel systems.”
